As a new field, prognostics or predictive diagnostics, is concerned with monitoring and assessing the operational status of electronic devices. The goal, beyond predicting the overall lifecycle of a device, is to determine the cause or causes of the eventual failure as well as the point in time where performance begins decreasing. To accomplish this, electronic prognostics rely on precursor signatures. These signatures indicate changes in operation that become metrics used to determine the “health status” of a digital device. Part of the on-going growth and maturation of the prognostics field involves identifying characteristics of an operating device that are predictive of performance, current health status, and remaining useful life. Once a predictive characteristic has been identified, a method must be developed that accurately and reliably extracts this characteristic for processing into a metric.
The best precursor signatures are those that can be correlated with failure but detected before performance is compromised. These sub-critical variations in performance give the most warning that makes them particularly useful as inputs for a prognostic health management (PHM) analysis platform or application.
At this time, prognostics or predictive diagnostics is a new field and in the process of discovery and maturation. The number of proven and reliable metrics is very limited. Examples of two existing metrics are Remaining Useful Lifetime (RUL) and State of Health (SoH). Prior efforts have involved destructive or invasive methodology to statistically forecast an expected device lifetime rather than monitor devices and gather the real-time data needed to determine actual lifecycles for specific devices in the field.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.